Overview

Dataset statistics

Number of variables18
Number of observations26458
Missing cells192211
Missing cells (%)40.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.6 MiB
Average record size in memory144.0 B

Variable types

Numeric10
Categorical8

Alerts

2013 is highly correlated with 2014 and 6 other fieldsHigh correlation
2014 is highly correlated with 2013 and 6 other fieldsHigh correlation
2015 is highly correlated with 2013 and 6 other fieldsHigh correlation
2016 is highly correlated with 2013 and 6 other fieldsHigh correlation
2017 is highly correlated with 2013 and 6 other fieldsHigh correlation
2018 is highly correlated with 2013 and 6 other fieldsHigh correlation
2019 is highly correlated with 2013 and 6 other fieldsHigh correlation
2020 is highly correlated with 2013 and 6 other fieldsHigh correlation
LABEL2020 is highly correlated with LABEL2013 and 6 other fieldsHigh correlation
LABEL2017 is highly correlated with LABEL2013 and 6 other fieldsHigh correlation
LABEL2016 is highly correlated with LABEL2013 and 6 other fieldsHigh correlation
LABEL2018 is highly correlated with LABEL2013 and 6 other fieldsHigh correlation
LABEL2019 is highly correlated with LABEL2013 and 6 other fieldsHigh correlation
LABEL2014 is highly correlated with LABEL2013 and 6 other fieldsHigh correlation
LABEL2013 is highly correlated with LABEL2014 and 6 other fieldsHigh correlation
LABEL2015 is highly correlated with LABEL2013 and 6 other fieldsHigh correlation
LABEL2013 has 24111 (91.1%) missing values Missing
LABEL2014 has 24081 (91.0%) missing values Missing
LABEL2015 has 24080 (91.0%) missing values Missing
LABEL2016 has 24080 (91.0%) missing values Missing
LABEL2017 has 24051 (90.9%) missing values Missing
LABEL2018 has 23977 (90.6%) missing values Missing
LABEL2019 has 23938 (90.5%) missing values Missing
LABEL2020 has 23893 (90.3%) missing values Missing

Reproduction

Analysis started2022-09-22 15:21:08.859755
Analysis finished2022-09-22 15:21:24.246598
Duration15.39 seconds
Software versionpandas-profiling v3.3.0
Download configurationconfig.json

Variables

LAT
Real number (ℝ≥0)

Distinct192
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.40020996
Minimum16.9375
Maximum17.8925
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size206.8 KiB
2022-09-22T20:51:24.316493image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum16.9375
5-th percentile17.0325
Q117.2175
median17.3975
Q317.5775
95-th percentile17.7825
Maximum17.8925
Range0.955
Interquartile range (IQR)0.36

Descriptive statistics

Standard deviation0.2316771372
Coefficient of variation (CV)0.01331461734
Kurtosis-0.9851021179
Mean17.40020996
Median Absolute Deviation (MAD)0.18
Skewness0.05136507924
Sum460374.755
Variance0.05367429588
MonotonicityNot monotonic
2022-09-22T20:51:24.439624image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.4775195
 
0.7%
17.4975195
 
0.7%
17.5075194
 
0.7%
17.4925194
 
0.7%
17.5025194
 
0.7%
17.3125193
 
0.7%
17.4875193
 
0.7%
17.3075193
 
0.7%
17.3175192
 
0.7%
17.4725192
 
0.7%
Other values (182)24523
92.7%
ValueCountFrequency (%)
16.93753
 
< 0.1%
16.94256
 
< 0.1%
16.947513
 
< 0.1%
16.952517
 
0.1%
16.957522
 
0.1%
16.962529
0.1%
16.967541
0.2%
16.972554
0.2%
16.977556
0.2%
16.982567
0.3%
ValueCountFrequency (%)
17.89253
 
< 0.1%
17.88759
 
< 0.1%
17.882510
 
< 0.1%
17.877511
 
< 0.1%
17.872518
0.1%
17.867528
0.1%
17.862531
0.1%
17.857536
0.1%
17.852541
0.2%
17.847542
0.2%

LON
Real number (ℝ≥0)

Distinct208
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78.47750121
Minimum78.0075
Maximum79.0425
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size206.8 KiB
2022-09-22T20:51:24.569662image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum78.0075
5-th percentile78.1125
Q178.2775
median78.4775
Q378.6675
95-th percentile78.8775
Maximum79.0425
Range1.035
Interquartile range (IQR)0.39

Descriptive statistics

Standard deviation0.2377368942
Coefficient of variation (CV)0.003029363709
Kurtosis-0.9561032173
Mean78.47750121
Median Absolute Deviation (MAD)0.195
Skewness0.1112356494
Sum2076357.727
Variance0.05651883084
MonotonicityIncreasing
2022-09-22T20:51:24.696723image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
78.5225180
 
0.7%
78.5475179
 
0.7%
78.5175179
 
0.7%
78.5125179
 
0.7%
78.4775179
 
0.7%
78.48251178
 
0.7%
78.5425178
 
0.7%
78.4625178
 
0.7%
78.4675177
 
0.7%
78.4725177
 
0.7%
Other values (198)24674
93.3%
ValueCountFrequency (%)
78.00753
 
< 0.1%
78.01254
 
< 0.1%
78.01756
 
< 0.1%
78.02259
 
< 0.1%
78.027512
 
< 0.1%
78.032515
 
0.1%
78.0375119
0.1%
78.042521
0.1%
78.047527
0.1%
78.0525145
0.2%
ValueCountFrequency (%)
79.04251
 
< 0.1%
79.037513
 
< 0.1%
79.03255
 
< 0.1%
79.02757
< 0.1%
79.022511
< 0.1%
79.017511
< 0.1%
79.012511
< 0.1%
79.007512
< 0.1%
79.002512
< 0.1%
78.9975114
0.1%

2013
Real number (ℝ≥0)

HIGH CORRELATION

Distinct25267
Distinct (%)95.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.308734992
Minimum0.05208
Maximum166.17392
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size206.8 KiB
2022-09-22T20:51:24.819838image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.05208
5-th percentile0.2298895
Q10.507775
median1.019525
Q32.60521
95-th percentile25.5592715
Maximum166.17392
Range166.12184
Interquartile range (IQR)2.097435

Descriptive statistics

Standard deviation10.0570163
Coefficient of variation (CV)2.334099525
Kurtosis26.64752601
Mean4.308734992
Median Absolute Deviation (MAD)0.64625
Skewness4.518544099
Sum114000.5104
Variance101.1435768
MonotonicityNot monotonic
2022-09-22T20:51:24.939131image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.33344
 
< 0.1%
1.064433
 
< 0.1%
1.398783
 
< 0.1%
0.287853
 
< 0.1%
1.143143
 
< 0.1%
0.215423
 
< 0.1%
0.848243
 
< 0.1%
0.419843
 
< 0.1%
0.273923
 
< 0.1%
0.426663
 
< 0.1%
Other values (25257)26427
99.9%
ValueCountFrequency (%)
0.052081
< 0.1%
0.06031
< 0.1%
0.062051
< 0.1%
0.062691
< 0.1%
0.062871
< 0.1%
0.063021
< 0.1%
0.064581
< 0.1%
0.065361
< 0.1%
0.066521
< 0.1%
0.069531
< 0.1%
ValueCountFrequency (%)
166.173921
< 0.1%
130.308171
< 0.1%
126.149011
< 0.1%
114.928271
< 0.1%
113.979981
< 0.1%
109.391171
< 0.1%
108.547571
< 0.1%
107.594111
< 0.1%
102.979591
< 0.1%
102.223341
< 0.1%

2014
Real number (ℝ≥0)

HIGH CORRELATION

Distinct25458
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.15077443
Minimum0.12183
Maximum139.58324
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size206.8 KiB
2022-09-22T20:51:25.064335image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.12183
5-th percentile0.325474
Q10.649865
median1.234595
Q33.04043
95-th percentile30.8670555
Maximum139.58324
Range139.46141
Interquartile range (IQR)2.390565

Descriptive statistics

Standard deviation11.79323074
Coefficient of variation (CV)2.289603417
Kurtosis21.708856
Mean5.15077443
Median Absolute Deviation (MAD)0.740895
Skewness4.232259071
Sum136279.1899
Variance139.0802912
MonotonicityNot monotonic
2022-09-22T20:51:25.185308image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.586984
 
< 0.1%
1.018014
 
< 0.1%
0.599934
 
< 0.1%
0.919263
 
< 0.1%
0.45553
 
< 0.1%
1.155673
 
< 0.1%
0.430663
 
< 0.1%
0.853643
 
< 0.1%
0.928873
 
< 0.1%
0.729193
 
< 0.1%
Other values (25448)26425
99.9%
ValueCountFrequency (%)
0.121831
< 0.1%
0.124011
< 0.1%
0.124261
< 0.1%
0.12751
< 0.1%
0.128121
< 0.1%
0.129941
< 0.1%
0.132861
< 0.1%
0.1331
< 0.1%
0.133721
< 0.1%
0.133941
< 0.1%
ValueCountFrequency (%)
139.583241
< 0.1%
121.876041
< 0.1%
121.024831
< 0.1%
120.889341
< 0.1%
118.581891
< 0.1%
118.076721
< 0.1%
113.619071
< 0.1%
113.577841
< 0.1%
113.565961
< 0.1%
112.348391
< 0.1%

2015
Real number (ℝ≥0)

HIGH CORRELATION

Distinct25428
Distinct (%)96.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.974485438
Minimum0.12
Maximum136.38487
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size206.8 KiB
2022-09-22T20:51:25.484513image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.12
5-th percentile0.3472595
Q10.6490275
median1.199135
Q33.024545
95-th percentile29.2776835
Maximum136.38487
Range136.26487
Interquartile range (IQR)2.3755175

Descriptive statistics

Standard deviation11.19135714
Coefficient of variation (CV)2.249751714
Kurtosis23.40962102
Mean4.974485438
Median Absolute Deviation (MAD)0.69983
Skewness4.308113632
Sum131614.9357
Variance125.2464747
MonotonicityNot monotonic
2022-09-22T20:51:25.609978image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.467843
 
< 0.1%
0.450533
 
< 0.1%
0.836163
 
< 0.1%
0.920413
 
< 0.1%
0.470843
 
< 0.1%
0.378083
 
< 0.1%
1.422953
 
< 0.1%
0.803063
 
< 0.1%
1.28053
 
< 0.1%
0.618523
 
< 0.1%
Other values (25418)26428
99.9%
ValueCountFrequency (%)
0.121
< 0.1%
0.135741
< 0.1%
0.145581
< 0.1%
0.146351
< 0.1%
0.152031
< 0.1%
0.155051
< 0.1%
0.158281
< 0.1%
0.159181
< 0.1%
0.159221
< 0.1%
0.162351
< 0.1%
ValueCountFrequency (%)
136.384871
< 0.1%
130.384321
< 0.1%
125.632121
< 0.1%
122.700941
< 0.1%
120.754381
< 0.1%
120.57781
< 0.1%
118.532121
< 0.1%
118.180781
< 0.1%
116.773871
< 0.1%
116.407751
< 0.1%

2016
Real number (ℝ≥0)

HIGH CORRELATION

Distinct25446
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.238069117
Minimum0.06581
Maximum146.73721
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size206.8 KiB
2022-09-22T20:51:25.739692image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.06581
5-th percentile0.270404
Q10.5684425
median1.12263
Q32.97593
95-th percentile31.54036
Maximum146.73721
Range146.6714
Interquartile range (IQR)2.4074875

Descriptive statistics

Standard deviation12.33840974
Coefficient of variation (CV)2.355526333
Kurtosis24.39708522
Mean5.238069117
Median Absolute Deviation (MAD)0.702765
Skewness4.399235269
Sum138588.8327
Variance152.2363548
MonotonicityNot monotonic
2022-09-22T20:51:25.861347image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.552195
 
< 0.1%
0.578094
 
< 0.1%
0.541313
 
< 0.1%
0.623173
 
< 0.1%
0.575953
 
< 0.1%
0.373143
 
< 0.1%
0.371123
 
< 0.1%
0.273353
 
< 0.1%
0.480493
 
< 0.1%
1.389253
 
< 0.1%
Other values (25436)26425
99.9%
ValueCountFrequency (%)
0.065811
< 0.1%
0.067611
< 0.1%
0.074281
< 0.1%
0.075011
< 0.1%
0.077371
< 0.1%
0.079661
< 0.1%
0.082781
< 0.1%
0.083471
< 0.1%
0.086541
< 0.1%
0.088871
< 0.1%
ValueCountFrequency (%)
146.737211
< 0.1%
143.603151
< 0.1%
135.641221
< 0.1%
134.230991
< 0.1%
133.405981
< 0.1%
132.419741
< 0.1%
131.700361
< 0.1%
128.47151
< 0.1%
124.496811
< 0.1%
124.298981
< 0.1%

2017
Real number (ℝ≥0)

HIGH CORRELATION

Distinct25428
Distinct (%)96.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.415846734
Minimum0.26693
Maximum132.48093
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size206.8 KiB
2022-09-22T20:51:25.987841image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.26693
5-th percentile0.4708905
Q10.7983475
median1.400305
Q33.432405
95-th percentile30.8972325
Maximum132.48093
Range132.214
Interquartile range (IQR)2.6340575

Descriptive statistics

Standard deviation11.65232079
Coefficient of variation (CV)2.151523365
Kurtosis22.6284539
Mean5.415846734
Median Absolute Deviation (MAD)0.761935
Skewness4.230513971
Sum143292.4729
Variance135.7765799
MonotonicityNot monotonic
2022-09-22T20:51:26.112454image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.681445
 
< 0.1%
0.729653
 
< 0.1%
0.770943
 
< 0.1%
0.423233
 
< 0.1%
0.699253
 
< 0.1%
0.534843
 
< 0.1%
0.761023
 
< 0.1%
1.934943
 
< 0.1%
0.705933
 
< 0.1%
0.983323
 
< 0.1%
Other values (25418)26426
99.9%
ValueCountFrequency (%)
0.266931
< 0.1%
0.267421
< 0.1%
0.269551
< 0.1%
0.269811
< 0.1%
0.275041
< 0.1%
0.275811
< 0.1%
0.27641
< 0.1%
0.276971
< 0.1%
0.278571
< 0.1%
0.279061
< 0.1%
ValueCountFrequency (%)
132.480931
< 0.1%
128.558911
< 0.1%
128.263351
< 0.1%
121.849391
< 0.1%
120.575871
< 0.1%
120.523491
< 0.1%
119.517331
< 0.1%
114.162281
< 0.1%
113.991071
< 0.1%
113.043391
< 0.1%

2018
Real number (ℝ≥0)

HIGH CORRELATION

Distinct25500
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.00496534
Minimum0.28841
Maximum134.52455
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size206.8 KiB
2022-09-22T20:51:26.239104image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.28841
5-th percentile0.517144
Q10.857955
median1.477525
Q33.5283725
95-th percentile25.755979
Maximum134.52455
Range134.23614
Interquartile range (IQR)2.6704175

Descriptive statistics

Standard deviation10.11081819
Coefficient of variation (CV)2.020157484
Kurtosis26.49696401
Mean5.00496534
Median Absolute Deviation (MAD)0.78819
Skewness4.426609412
Sum132421.373
Variance102.2286445
MonotonicityNot monotonic
2022-09-22T20:51:26.365453image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.776194
 
< 0.1%
1.071884
 
< 0.1%
1.407314
 
< 0.1%
0.997134
 
< 0.1%
0.928513
 
< 0.1%
0.858933
 
< 0.1%
0.406563
 
< 0.1%
0.763253
 
< 0.1%
0.8243
 
< 0.1%
0.928863
 
< 0.1%
Other values (25490)26424
99.9%
ValueCountFrequency (%)
0.288411
< 0.1%
0.29051
< 0.1%
0.293321
< 0.1%
0.293821
< 0.1%
0.294881
< 0.1%
0.295631
< 0.1%
0.297161
< 0.1%
0.305431
< 0.1%
0.306191
< 0.1%
0.307531
< 0.1%
ValueCountFrequency (%)
134.524551
< 0.1%
128.173571
< 0.1%
127.686911
< 0.1%
126.462391
< 0.1%
114.878911
< 0.1%
113.720361
< 0.1%
113.575331
< 0.1%
112.890731
< 0.1%
112.415361
< 0.1%
111.312261
< 0.1%

2019
Real number (ℝ≥0)

HIGH CORRELATION

Distinct25679
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.622901714
Minimum0.26474
Maximum115.83806
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size206.8 KiB
2022-09-22T20:51:26.493737image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.26474
5-th percentile0.5767465
Q10.99589
median1.74988
Q34.27217
95-th percentile28.357114
Maximum115.83806
Range115.57332
Interquartile range (IQR)3.27628

Descriptive statistics

Standard deviation10.63871867
Coefficient of variation (CV)1.892033546
Kurtosis19.69356439
Mean5.622901714
Median Absolute Deviation (MAD)0.96381
Skewness3.932800325
Sum148770.7335
Variance113.1823349
MonotonicityNot monotonic
2022-09-22T20:51:26.615201image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.855284
 
< 0.1%
0.802144
 
< 0.1%
1.031323
 
< 0.1%
0.855343
 
< 0.1%
0.92093
 
< 0.1%
0.574433
 
< 0.1%
1.216493
 
< 0.1%
0.992273
 
< 0.1%
1.498433
 
< 0.1%
0.964423
 
< 0.1%
Other values (25669)26426
99.9%
ValueCountFrequency (%)
0.264741
< 0.1%
0.282821
< 0.1%
0.285071
< 0.1%
0.286131
< 0.1%
0.291941
< 0.1%
0.292691
< 0.1%
0.292741
< 0.1%
0.295131
< 0.1%
0.295581
< 0.1%
0.296271
< 0.1%
ValueCountFrequency (%)
115.838061
< 0.1%
113.086571
< 0.1%
107.582331
< 0.1%
106.775991
< 0.1%
104.566141
< 0.1%
104.196081
< 0.1%
102.703741
< 0.1%
102.208591
< 0.1%
101.593391
< 0.1%
98.671941
< 0.1%

2020
Real number (ℝ≥0)

HIGH CORRELATION

Distinct25669
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.438204924
Minimum0.32418
Maximum106.10519
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size206.8 KiB
2022-09-22T20:51:26.744208image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.32418
5-th percentile0.6042885
Q11.0038125
median1.6962
Q34.1373525
95-th percentile27.1759345
Maximum106.10519
Range105.78101
Interquartile range (IQR)3.13354

Descriptive statistics

Standard deviation10.05724696
Coefficient of variation (CV)1.849368882
Kurtosis18.32798733
Mean5.438204924
Median Absolute Deviation (MAD)0.89431
Skewness3.798516052
Sum143884.0259
Variance101.1482165
MonotonicityNot monotonic
2022-09-22T20:51:26.868918image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.714754
 
< 0.1%
0.776633
 
< 0.1%
0.716543
 
< 0.1%
0.746753
 
< 0.1%
0.839293
 
< 0.1%
1.075273
 
< 0.1%
0.92533
 
< 0.1%
1.112073
 
< 0.1%
0.778913
 
< 0.1%
1.282053
 
< 0.1%
Other values (25659)26427
99.9%
ValueCountFrequency (%)
0.324181
< 0.1%
0.332621
< 0.1%
0.33611
< 0.1%
0.337321
< 0.1%
0.339121
< 0.1%
0.34161
< 0.1%
0.343351
< 0.1%
0.345811
< 0.1%
0.345841
< 0.1%
0.34671
< 0.1%
ValueCountFrequency (%)
106.105191
< 0.1%
100.36031
< 0.1%
98.420071
< 0.1%
98.357321
< 0.1%
96.515791
< 0.1%
96.302961
< 0.1%
96.207091
< 0.1%
95.390721
< 0.1%
95.012471
< 0.1%
94.614461
< 0.1%

LABEL2013
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.1%
Missing24111
Missing (%)91.1%
Memory size206.8 KiB
Urban
2320 
Water
 
27

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters11735
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUrban
2nd rowUrban
3rd rowUrban
4th rowUrban
5th rowUrban

Common Values

ValueCountFrequency (%)
Urban2320
 
8.8%
Water27
 
0.1%
(Missing)24111
91.1%

Length

2022-09-22T20:51:26.978015image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-22T20:51:27.062309image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
urban2320
98.8%
water27
 
1.2%

Most occurring characters

ValueCountFrequency (%)
r2347
20.0%
a2347
20.0%
U2320
19.8%
b2320
19.8%
n2320
19.8%
W27
 
0.2%
t27
 
0.2%
e27
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter9388
80.0%
Uppercase Letter2347
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r2347
25.0%
a2347
25.0%
b2320
24.7%
n2320
24.7%
t27
 
0.3%
e27
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
U2320
98.8%
W27
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
Latin11735
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r2347
20.0%
a2347
20.0%
U2320
19.8%
b2320
19.8%
n2320
19.8%
W27
 
0.2%
t27
 
0.2%
e27
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII11735
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r2347
20.0%
a2347
20.0%
U2320
19.8%
b2320
19.8%
n2320
19.8%
W27
 
0.2%
t27
 
0.2%
e27
 
0.2%

LABEL2014
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.1%
Missing24081
Missing (%)91.0%
Memory size206.8 KiB
Urban
2350 
Water
 
27

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters11885
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUrban
2nd rowUrban
3rd rowUrban
4th rowUrban
5th rowUrban

Common Values

ValueCountFrequency (%)
Urban2350
 
8.9%
Water27
 
0.1%
(Missing)24081
91.0%

Length

2022-09-22T20:51:27.134177image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-22T20:51:27.218571image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
urban2350
98.9%
water27
 
1.1%

Most occurring characters

ValueCountFrequency (%)
r2377
20.0%
a2377
20.0%
U2350
19.8%
b2350
19.8%
n2350
19.8%
W27
 
0.2%
t27
 
0.2%
e27
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter9508
80.0%
Uppercase Letter2377
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r2377
25.0%
a2377
25.0%
b2350
24.7%
n2350
24.7%
t27
 
0.3%
e27
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
U2350
98.9%
W27
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Latin11885
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r2377
20.0%
a2377
20.0%
U2350
19.8%
b2350
19.8%
n2350
19.8%
W27
 
0.2%
t27
 
0.2%
e27
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII11885
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r2377
20.0%
a2377
20.0%
U2350
19.8%
b2350
19.8%
n2350
19.8%
W27
 
0.2%
t27
 
0.2%
e27
 
0.2%

LABEL2015
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.1%
Missing24080
Missing (%)91.0%
Memory size206.8 KiB
Urban
2356 
Water
 
22

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters11890
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUrban
2nd rowUrban
3rd rowUrban
4th rowUrban
5th rowUrban

Common Values

ValueCountFrequency (%)
Urban2356
 
8.9%
Water22
 
0.1%
(Missing)24080
91.0%

Length

2022-09-22T20:51:27.294514image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-22T20:51:27.381225image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
urban2356
99.1%
water22
 
0.9%

Most occurring characters

ValueCountFrequency (%)
r2378
20.0%
a2378
20.0%
U2356
19.8%
b2356
19.8%
n2356
19.8%
W22
 
0.2%
t22
 
0.2%
e22
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter9512
80.0%
Uppercase Letter2378
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r2378
25.0%
a2378
25.0%
b2356
24.8%
n2356
24.8%
t22
 
0.2%
e22
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
U2356
99.1%
W22
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Latin11890
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r2378
20.0%
a2378
20.0%
U2356
19.8%
b2356
19.8%
n2356
19.8%
W22
 
0.2%
t22
 
0.2%
e22
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII11890
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r2378
20.0%
a2378
20.0%
U2356
19.8%
b2356
19.8%
n2356
19.8%
W22
 
0.2%
t22
 
0.2%
e22
 
0.2%

LABEL2016
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.1%
Missing24080
Missing (%)91.0%
Memory size206.8 KiB
Urban
2360 
Water
 
18

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters11890
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUrban
2nd rowUrban
3rd rowUrban
4th rowUrban
5th rowUrban

Common Values

ValueCountFrequency (%)
Urban2360
 
8.9%
Water18
 
0.1%
(Missing)24080
91.0%

Length

2022-09-22T20:51:27.453166image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-22T20:51:27.541080image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
urban2360
99.2%
water18
 
0.8%

Most occurring characters

ValueCountFrequency (%)
r2378
20.0%
a2378
20.0%
U2360
19.8%
b2360
19.8%
n2360
19.8%
W18
 
0.2%
t18
 
0.2%
e18
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter9512
80.0%
Uppercase Letter2378
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r2378
25.0%
a2378
25.0%
b2360
24.8%
n2360
24.8%
t18
 
0.2%
e18
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
U2360
99.2%
W18
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Latin11890
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r2378
20.0%
a2378
20.0%
U2360
19.8%
b2360
19.8%
n2360
19.8%
W18
 
0.2%
t18
 
0.2%
e18
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII11890
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r2378
20.0%
a2378
20.0%
U2360
19.8%
b2360
19.8%
n2360
19.8%
W18
 
0.2%
t18
 
0.2%
e18
 
0.2%

LABEL2017
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.1%
Missing24051
Missing (%)90.9%
Memory size206.8 KiB
Urban
2371 
Water
 
36

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters12035
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUrban
2nd rowUrban
3rd rowUrban
4th rowUrban
5th rowUrban

Common Values

ValueCountFrequency (%)
Urban2371
 
9.0%
Water36
 
0.1%
(Missing)24051
90.9%

Length

2022-09-22T20:51:27.616701image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-22T20:51:27.703875image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
urban2371
98.5%
water36
 
1.5%

Most occurring characters

ValueCountFrequency (%)
r2407
20.0%
a2407
20.0%
U2371
19.7%
b2371
19.7%
n2371
19.7%
W36
 
0.3%
t36
 
0.3%
e36
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter9628
80.0%
Uppercase Letter2407
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r2407
25.0%
a2407
25.0%
b2371
24.6%
n2371
24.6%
t36
 
0.4%
e36
 
0.4%
Uppercase Letter
ValueCountFrequency (%)
U2371
98.5%
W36
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
Latin12035
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r2407
20.0%
a2407
20.0%
U2371
19.7%
b2371
19.7%
n2371
19.7%
W36
 
0.3%
t36
 
0.3%
e36
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII12035
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r2407
20.0%
a2407
20.0%
U2371
19.7%
b2371
19.7%
n2371
19.7%
W36
 
0.3%
t36
 
0.3%
e36
 
0.3%

LABEL2018
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.1%
Missing23977
Missing (%)90.6%
Memory size206.8 KiB
Urban
2432 
Water
 
49

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters12405
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUrban
2nd rowUrban
3rd rowUrban
4th rowUrban
5th rowUrban

Common Values

ValueCountFrequency (%)
Urban2432
 
9.2%
Water49
 
0.2%
(Missing)23977
90.6%

Length

2022-09-22T20:51:27.778629image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-22T20:51:27.867463image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
urban2432
98.0%
water49
 
2.0%

Most occurring characters

ValueCountFrequency (%)
r2481
20.0%
a2481
20.0%
U2432
19.6%
b2432
19.6%
n2432
19.6%
W49
 
0.4%
t49
 
0.4%
e49
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter9924
80.0%
Uppercase Letter2481
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r2481
25.0%
a2481
25.0%
b2432
24.5%
n2432
24.5%
t49
 
0.5%
e49
 
0.5%
Uppercase Letter
ValueCountFrequency (%)
U2432
98.0%
W49
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
Latin12405
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r2481
20.0%
a2481
20.0%
U2432
19.6%
b2432
19.6%
n2432
19.6%
W49
 
0.4%
t49
 
0.4%
e49
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII12405
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r2481
20.0%
a2481
20.0%
U2432
19.6%
b2432
19.6%
n2432
19.6%
W49
 
0.4%
t49
 
0.4%
e49
 
0.4%

LABEL2019
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.1%
Missing23938
Missing (%)90.5%
Memory size206.8 KiB
Urban
2485 
Water
 
35

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters12600
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUrban
2nd rowUrban
3rd rowUrban
4th rowUrban
5th rowUrban

Common Values

ValueCountFrequency (%)
Urban2485
 
9.4%
Water35
 
0.1%
(Missing)23938
90.5%

Length

2022-09-22T20:51:28.102093image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-22T20:51:28.191218image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
urban2485
98.6%
water35
 
1.4%

Most occurring characters

ValueCountFrequency (%)
r2520
20.0%
a2520
20.0%
U2485
19.7%
b2485
19.7%
n2485
19.7%
W35
 
0.3%
t35
 
0.3%
e35
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter10080
80.0%
Uppercase Letter2520
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r2520
25.0%
a2520
25.0%
b2485
24.7%
n2485
24.7%
t35
 
0.3%
e35
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
U2485
98.6%
W35
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
Latin12600
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r2520
20.0%
a2520
20.0%
U2485
19.7%
b2485
19.7%
n2485
19.7%
W35
 
0.3%
t35
 
0.3%
e35
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII12600
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r2520
20.0%
a2520
20.0%
U2485
19.7%
b2485
19.7%
n2485
19.7%
W35
 
0.3%
t35
 
0.3%
e35
 
0.3%

LABEL2020
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.1%
Missing23893
Missing (%)90.3%
Memory size206.8 KiB
Urban
2550 
Water
 
15

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters12825
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUrban
2nd rowUrban
3rd rowUrban
4th rowUrban
5th rowUrban

Common Values

ValueCountFrequency (%)
Urban2550
 
9.6%
Water15
 
0.1%
(Missing)23893
90.3%

Length

2022-09-22T20:51:28.266457image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-22T20:51:28.356004image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
urban2550
99.4%
water15
 
0.6%

Most occurring characters

ValueCountFrequency (%)
r2565
20.0%
a2565
20.0%
U2550
19.9%
b2550
19.9%
n2550
19.9%
W15
 
0.1%
t15
 
0.1%
e15
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter10260
80.0%
Uppercase Letter2565
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r2565
25.0%
a2565
25.0%
b2550
24.9%
n2550
24.9%
t15
 
0.1%
e15
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
U2550
99.4%
W15
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Latin12825
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r2565
20.0%
a2565
20.0%
U2550
19.9%
b2550
19.9%
n2550
19.9%
W15
 
0.1%
t15
 
0.1%
e15
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII12825
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r2565
20.0%
a2565
20.0%
U2550
19.9%
b2550
19.9%
n2550
19.9%
W15
 
0.1%
t15
 
0.1%
e15
 
0.1%

Interactions

2022-09-22T20:51:22.234037image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:10.755633image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:11.826780image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:12.941015image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:14.331501image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:15.665221image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:17.098618image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:18.493034image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:19.660340image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:20.893355image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:22.341353image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:10.857522image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:11.929921image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:13.049935image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:14.441339image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:15.874373image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:17.239755image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:18.599842image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:19.772187image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:21.002185image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:22.454200image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:10.963594image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:12.039377image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:13.163207image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:14.574098image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:16.013938image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:17.356039image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:18.713116image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:19.894838image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:21.115147image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:22.564470image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:11.069392image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:12.147060image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:13.279131image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:14.708517image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:16.223882image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:17.488325image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:18.825646image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:20.010394image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:21.228320image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:22.682530image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:11.177374image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:12.260575image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:13.406932image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:14.833002image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:16.341754image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:17.610461image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:18.941888image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:20.156790image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:21.343790image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:22.795661image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:11.288819image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:12.372624image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:13.536309image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:14.949281image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:16.467409image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:17.737632image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:19.091861image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:20.294387image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:21.458958image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:22.909898image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:11.400195image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:12.487454image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:13.667443image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:15.069866image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:16.589542image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:17.855808image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:19.209774image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:20.421230image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:21.573633image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:23.018053image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:11.507160image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:12.597211image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:13.797659image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:15.199333image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:16.738666image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:17.971540image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:19.325642image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:20.540270image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:21.687029image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:23.133425image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:11.615490image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:12.714131image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:14.102268image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:15.369433image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:16.856567image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:18.095849image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:19.439526image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:20.663437image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:21.830787image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:23.241399image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:11.722816image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:12.829489image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:14.221715image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:15.500846image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:16.970097image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:18.382903image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:19.552747image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:20.779754image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:21.943516image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-09-22T20:51:28.456901image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-09-22T20:51:28.608158image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-09-22T20:51:28.754894image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-09-22T20:51:28.890187image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-09-22T20:51:29.032244image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-09-22T20:51:23.450926image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-09-22T20:51:23.758457image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-09-22T20:51:23.976295image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-09-22T20:51:24.129309image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

LATLON20132014201520162017201820192020LABEL2013LABEL2014LABEL2015LABEL2016LABEL2017LABEL2018LABEL2019LABEL2020
017.322578.00750.651220.927040.579980.474460.629410.820970.675070.84478NoneNoneNoneNoneNoneNoneNoneNone
117.327578.00750.657910.860680.434800.386130.564190.709460.630690.66205NoneNoneNoneNoneNoneNoneNoneNone
217.332578.00750.393880.599870.325110.304320.495520.479520.546660.55795NoneNoneNoneNoneNoneNoneNoneNone
317.322578.01250.357300.484970.349250.344150.603150.665580.644370.66512NoneNoneNoneNoneNoneNoneNoneNone
417.327578.01250.437080.671960.415830.359320.646270.629490.668980.59453NoneNoneNoneNoneNoneNoneNoneNone
517.332578.01250.643160.628180.459950.428250.673320.542010.594120.58083NoneNoneNoneNoneNoneNoneNoneNone
617.337578.01250.445300.442510.298750.318460.541800.464200.546600.53525NoneNoneNoneNoneNoneNoneNoneNone
717.312578.01750.279630.406800.442090.366390.593860.606070.761010.91053NoneNoneNoneNoneNoneNoneNoneNone
817.317578.01750.279760.402350.355370.370370.617410.626850.731330.82290NoneNoneNoneNoneNoneNoneNoneNone
917.322578.01750.373520.484240.351260.353670.662000.663910.729780.72779NoneNoneNoneNoneNoneNoneNoneNone

Last rows

LATLON20132014201520162017201820192020LABEL2013LABEL2014LABEL2015LABEL2016LABEL2017LABEL2018LABEL2019LABEL2020
2644817.517579.027500.209100.235970.304460.160960.357680.406430.557160.64827NoneNoneNoneNoneNoneNoneNoneNone
2644917.492579.032500.173140.204940.317230.231970.382490.406070.595900.63860NoneNoneNoneNoneNoneNoneNoneNone
2645017.497579.032500.193410.231430.359850.188630.381350.420450.535920.63415NoneNoneNoneNoneNoneNoneNoneNone
2645117.502579.032500.355640.385620.527250.330200.556220.626450.916130.88535NoneNoneNoneNoneNoneNoneNoneNone
2645217.507579.032500.528670.507940.539790.372670.590580.659870.970911.13857NoneNoneNoneNoneNoneNoneNoneNone
2645317.512579.032500.298210.296150.344170.182230.406430.453740.603130.79152NoneNoneNoneNoneNoneNoneNoneNone
2645417.497579.037510.177440.217220.323960.228690.361230.421290.486990.57725NoneNoneNoneNoneNoneNoneNoneNone
2645517.502579.037510.189880.264080.327590.254840.402260.426680.599390.62796NoneNoneNoneNoneNoneNoneNoneNone
2645617.507579.037510.230610.270150.346060.221630.399200.439230.587360.76583NoneNoneNoneNoneNoneNoneNoneNone
2645717.507579.042500.152620.213830.309070.156850.353200.407340.544820.60583NoneNoneNoneNoneNoneNoneNoneNone